Abstract: A method for predicting water clarity at a plurality of water depths for a location including providing training data to a neural network, the training data representative of water measurements at the location, thereafter receiving inputs including temperature, salinity, tidal information, water depth, and sediment data, and generating values for optical attenuation at a wavelength at a plurality of depths. In one embodiment, a default cloudy day algorithm operates at all times and a clear sky algorithm operates only when clear satellite images are available.

Abstract: The presently disclosed invention relates to an apparatus and method for collecting satellite images of bodies of water such as the oceans and removing interfering clouds from the satellite images to improve the accuracy of the images. More particularly, the presently disclosed invention relates to the use of an apparatus and method to extract cloud edge information through local segmentation of the image and a procedure to discriminate between cloud free and cloud contaminated pixels. The cloud contaminated pixels are then removed from daytime and nighttime 1-km advanced very high resolution radiometer (AVHRR) satellite image.